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PaulJonasJost merged 2 commits into from
Dec 19, 2025
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Using PEtab constants #208

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9651714
Using PEtab constants where found
PaulJonasJost Dec 16, 2025
c3bab95
Amibguous imprt in C.py of constants
PaulJonasJost Dec 19, 2025
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231 changes: 140 additions & 91 deletions src/petab_gui/C.py
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Original file line number Diff line number Diff line change
@@ -1,6 +1,7 @@
"""Constants for the PEtab edit GUI."""

import numpy as np
import petab.v1.C as PETAB_C

#: Application name
APP_NAME = "PEtab-GUI"
Expand All @@ -11,73 +12,94 @@

COLUMNS = {
"measurement": {
"observableId": {"type": np.object_, "optional": False},
"preequilibrationConditionId": {"type": np.object_, "optional": True},
"simulationConditionId": {"type": np.object_, "optional": False},
"time": {"type": np.float64, "optional": False},
"measurement": {"type": np.float64, "optional": False},
"observableParameters": {"type": np.object_, "optional": True},
"noiseParameters": {"type": np.object_, "optional": True},
"datasetId": {"type": np.object_, "optional": True},
"replicateId": {"type": np.object_, "optional": True},
PETAB_C.OBSERVABLE_ID: {"type": np.object_, "optional": False},
PETAB_C.PREEQUILIBRATION_CONDITION_ID: {
"type": np.object_,
"optional": True,
},
PETAB_C.SIMULATION_CONDITION_ID: {
"type": np.object_,
"optional": False,
},
PETAB_C.TIME: {"type": np.float64, "optional": False},
PETAB_C.MEASUREMENT: {"type": np.float64, "optional": False},
PETAB_C.OBSERVABLE_PARAMETERS: {"type": np.object_, "optional": True},
PETAB_C.NOISE_PARAMETERS: {"type": np.object_, "optional": True},
PETAB_C.DATASET_ID: {"type": np.object_, "optional": True},
PETAB_C.REPLICATE_ID: {"type": np.object_, "optional": True},
},
"simulation": {
"observableId": {"type": np.object_, "optional": False},
"preequilibrationConditionId": {"type": np.object_, "optional": True},
"simulationConditionId": {"type": np.object_, "optional": False},
"time": {"type": np.float64, "optional": False},
"simulation": {"type": np.float64, "optional": False},
"observableParameters": {"type": np.object_, "optional": True},
"noiseParameters": {"type": np.object_, "optional": True},
"datasetId": {"type": np.object_, "optional": True},
"replicateId": {"type": np.object_, "optional": True},
PETAB_C.OBSERVABLE_ID: {"type": np.object_, "optional": False},
PETAB_C.PREEQUILIBRATION_CONDITION_ID: {
"type": np.object_,
"optional": True,
},
PETAB_C.SIMULATION_CONDITION_ID: {
"type": np.object_,
"optional": False,
},
PETAB_C.TIME: {"type": np.float64, "optional": False},
PETAB_C.SIMULATION: {"type": np.float64, "optional": False},
PETAB_C.OBSERVABLE_PARAMETERS: {"type": np.object_, "optional": True},
PETAB_C.NOISE_PARAMETERS: {"type": np.object_, "optional": True},
PETAB_C.DATASET_ID: {"type": np.object_, "optional": True},
PETAB_C.REPLICATE_ID: {"type": np.object_, "optional": True},
},
"observable": {
"observableId": {"type": np.object_, "optional": False},
"observableName": {"type": np.object_, "optional": True},
"observableFormula": {"type": np.object_, "optional": False},
"observableTransformation": {"type": np.object_, "optional": True},
"noiseFormula": {"type": np.object_, "optional": False},
"noiseDistribution": {"type": np.object_, "optional": True},
PETAB_C.OBSERVABLE_ID: {"type": np.object_, "optional": False},
PETAB_C.OBSERVABLE_NAME: {"type": np.object_, "optional": True},
PETAB_C.OBSERVABLE_FORMULA: {"type": np.object_, "optional": False},
PETAB_C.OBSERVABLE_TRANSFORMATION: {
"type": np.object_,
"optional": True,
},
PETAB_C.NOISE_FORMULA: {"type": np.object_, "optional": False},
PETAB_C.NOISE_DISTRIBUTION: {"type": np.object_, "optional": True},
},
"parameter": {
"parameterId": {"type": np.object_, "optional": False},
"parameterName": {"type": np.object_, "optional": True},
"parameterScale": {"type": np.object_, "optional": False},
"lowerBound": {"type": np.float64, "optional": False},
"upperBound": {"type": np.float64, "optional": False},
"nominalValue": {"type": np.float64, "optional": False},
"estimate": {"type": np.object_, "optional": False},
"initializationPriorType": {"type": np.object_, "optional": True},
"initializationPriorParameters": {
PETAB_C.PARAMETER_ID: {"type": np.object_, "optional": False},
PETAB_C.PARAMETER_NAME: {"type": np.object_, "optional": True},
PETAB_C.PARAMETER_SCALE: {"type": np.object_, "optional": False},
PETAB_C.LOWER_BOUND: {"type": np.float64, "optional": False},
PETAB_C.UPPER_BOUND: {"type": np.float64, "optional": False},
PETAB_C.NOMINAL_VALUE: {"type": np.float64, "optional": False},
PETAB_C.ESTIMATE: {"type": np.object_, "optional": False},
PETAB_C.INITIALIZATION_PRIOR_TYPE: {
"type": np.object_,
"optional": True,
},
PETAB_C.INITIALIZATION_PRIOR_PARAMETERS: {
"type": np.object_,
"optional": True,
},
PETAB_C.OBJECTIVE_PRIOR_TYPE: {"type": np.object_, "optional": True},
PETAB_C.OBJECTIVE_PRIOR_PARAMETERS: {
"type": np.object_,
"optional": True,
},
"objectivePriorType": {"type": np.object_, "optional": True},
"objectivePriorParameters": {"type": np.object_, "optional": True},
},
"condition": {
"conditionId": {"type": np.object_, "optional": False},
"conditionName": {"type": np.object_, "optional": False},
PETAB_C.CONDITION_ID: {"type": np.object_, "optional": False},
PETAB_C.CONDITION_NAME: {"type": np.object_, "optional": False},
},
"visualization": {
"plotId": {"type": np.object_, "optional": False},
"plotName": {"type": np.object_, "optional": True},
"plotTypeSimulation": {
PETAB_C.PLOT_ID: {"type": np.object_, "optional": False},
PETAB_C.PLOT_NAME: {"type": np.object_, "optional": True},
PETAB_C.PLOT_TYPE_SIMULATION: {
"type": np.object_,
"optional": True,
},
"plotTypeData": {"type": np.object_, "optional": True},
"datasetId": {"type": np.object_, "optional": True},
"xValues": {"type": np.object_, "optional": True},
"xOffset": {"type": np.float64, "optional": True},
"xLabel": {"type": np.object_, "optional": True},
"xScale": {"type": np.object_, "optional": True},
"yValues": {"type": np.object_, "optional": True},
"yOffset": {"type": np.float64, "optional": True},
"yLabel": {"type": np.object_, "optional": True},
"yScale": {"type": np.object_, "optional": True},
"legendEntry": {"type": np.object_, "optional": True},
PETAB_C.PLOT_TYPE_DATA: {"type": np.object_, "optional": True},
PETAB_C.DATASET_ID: {"type": np.object_, "optional": True},
PETAB_C.X_VALUES: {"type": np.object_, "optional": True},
PETAB_C.X_OFFSET: {"type": np.float64, "optional": True},
PETAB_C.X_LABEL: {"type": np.object_, "optional": True},
PETAB_C.X_SCALE: {"type": np.object_, "optional": True},
PETAB_C.Y_VALUES: {"type": np.object_, "optional": True},
PETAB_C.Y_OFFSET: {"type": np.float64, "optional": True},
PETAB_C.Y_LABEL: {"type": np.object_, "optional": True},
PETAB_C.Y_SCALE: {"type": np.object_, "optional": True},
PETAB_C.LEGEND_ENTRY: {"type": np.object_, "optional": True},
},
}

Expand Down Expand Up @@ -126,36 +148,48 @@

# Default Configurations of Default Values
ALLOWED_STRATEGIES_OBS = {
"observableId": STRATEGIES_DEFAULT,
"observableName": STRATEGIES_DEFAULT,
"observableFormula": STRATEGIES_DEFAULT,
"observableTransformation": [USE_DEFAULT, NO_DEFAULT, MODE],
"noiseFormula": [COPY_FROM, USE_DEFAULT, NO_DEFAULT, MODE],
"noiseDistribution": [USE_DEFAULT, NO_DEFAULT, MODE],
PETAB_C.OBSERVABLE_ID: STRATEGIES_DEFAULT,
PETAB_C.OBSERVABLE_NAME: STRATEGIES_DEFAULT,
PETAB_C.OBSERVABLE_FORMULA: STRATEGIES_DEFAULT,
PETAB_C.OBSERVABLE_TRANSFORMATION: [USE_DEFAULT, NO_DEFAULT, MODE],
PETAB_C.NOISE_FORMULA: [COPY_FROM, USE_DEFAULT, NO_DEFAULT, MODE],
PETAB_C.NOISE_DISTRIBUTION: [USE_DEFAULT, NO_DEFAULT, MODE],
}
ALLOWED_STRATEGIES_PAR = {
"parameterId": STRATEGIES_DEFAULT,
"parameterName": STRATEGIES_DEFAULT,
"parameterScale": [USE_DEFAULT, NO_DEFAULT, MODE],
"lowerBound": [MIN_COLUMN, MAX_COLUMN, USE_DEFAULT, NO_DEFAULT, MODE],
"upperBound": [MAX_COLUMN, MAX_COLUMN, USE_DEFAULT, NO_DEFAULT, MODE],
"nominalValue": [USE_DEFAULT, NO_DEFAULT, SBML_LOOK],
"estimate": [USE_DEFAULT, NO_DEFAULT, MODE],
PETAB_C.PARAMETER_ID: STRATEGIES_DEFAULT,
PETAB_C.PARAMETER_NAME: STRATEGIES_DEFAULT,
PETAB_C.PARAMETER_SCALE: [USE_DEFAULT, NO_DEFAULT, MODE],
PETAB_C.LOWER_BOUND: [
MIN_COLUMN,
MAX_COLUMN,
USE_DEFAULT,
NO_DEFAULT,
MODE,
],
PETAB_C.UPPER_BOUND: [
MAX_COLUMN,
MAX_COLUMN,
USE_DEFAULT,
NO_DEFAULT,
MODE,
],
PETAB_C.NOMINAL_VALUE: [USE_DEFAULT, NO_DEFAULT, SBML_LOOK],
PETAB_C.ESTIMATE: [USE_DEFAULT, NO_DEFAULT, MODE],
}
ALLOWED_STRATEGIES_COND = {
"conditionId": STRATEGIES_DEFAULT,
"conditionName": STRATEGIES_DEFAULT,
PETAB_C.CONDITION_ID: STRATEGIES_DEFAULT,
PETAB_C.CONDITION_NAME: STRATEGIES_DEFAULT,
}
ALLOWED_STRATEGIES_MEAS = {
"observableId": STRATEGIES_DEFAULT,
"preequilibrationConditionId": STRATEGIES_DEFAULT_EXT,
"simulationConditionId": STRATEGIES_DEFAULT_EXT,
"time": [NO_DEFAULT, USE_DEFAULT, MODE],
"measurement": [NO_DEFAULT, USE_DEFAULT, MODE],
"observableParameters": STRATEGIES_DEFAULT_EXT,
"noiseParameters": STRATEGIES_DEFAULT_EXT,
"datasetId": [COPY_FROM, USE_DEFAULT, NO_DEFAULT, MODE],
"replicateId": [COPY_FROM, USE_DEFAULT, NO_DEFAULT, MODE],
PETAB_C.OBSERVABLE_ID: STRATEGIES_DEFAULT,
PETAB_C.PREEQUILIBRATION_CONDITION_ID: STRATEGIES_DEFAULT_EXT,
PETAB_C.SIMULATION_CONDITION_ID: STRATEGIES_DEFAULT_EXT,
PETAB_C.TIME: [NO_DEFAULT, USE_DEFAULT, MODE],
PETAB_C.MEASUREMENT: [NO_DEFAULT, USE_DEFAULT, MODE],
PETAB_C.OBSERVABLE_PARAMETERS: STRATEGIES_DEFAULT_EXT,
PETAB_C.NOISE_PARAMETERS: STRATEGIES_DEFAULT_EXT,
PETAB_C.DATASET_ID: [COPY_FROM, USE_DEFAULT, NO_DEFAULT, MODE],
PETAB_C.REPLICATE_ID: [COPY_FROM, USE_DEFAULT, NO_DEFAULT, MODE],
}
ALLOWED_STRATEGIES = {
"observable": ALLOWED_STRATEGIES_OBS,
Expand All @@ -164,34 +198,49 @@
"measurement": ALLOWED_STRATEGIES_MEAS,
}
DEFAULT_OBS_CONFIG = {
"observableId": {
PETAB_C.OBSERVABLE_ID: {
"strategy": COPY_FROM,
SOURCE_COLUMN: "observableFormula",
SOURCE_COLUMN: PETAB_C.OBSERVABLE_FORMULA,
DEFAULT_VALUE: "new_observable",
},
"observableName": {"strategy": COPY_FROM, SOURCE_COLUMN: "observableId"},
"noiseFormula": {"strategy": USE_DEFAULT, DEFAULT_VALUE: 1},
"observableTransformation": {
PETAB_C.OBSERVABLE_NAME: {
"strategy": COPY_FROM,
SOURCE_COLUMN: PETAB_C.OBSERVABLE_ID,
},
PETAB_C.NOISE_FORMULA: {"strategy": USE_DEFAULT, DEFAULT_VALUE: 1},
PETAB_C.OBSERVABLE_TRANSFORMATION: {
"strategy": USE_DEFAULT,
DEFAULT_VALUE: PETAB_C.LIN,
},
PETAB_C.NOISE_DISTRIBUTION: {
"strategy": USE_DEFAULT,
DEFAULT_VALUE: "lin",
DEFAULT_VALUE: PETAB_C.NORMAL,
},
"noiseDistribution": {"strategy": USE_DEFAULT, DEFAULT_VALUE: "normal"},
}
DEFAULT_PAR_CONFIG = {
"parameterName": {
PETAB_C.PARAMETER_NAME: {
"strategy": COPY_FROM,
SOURCE_COLUMN: "parameterId",
SOURCE_COLUMN: PETAB_C.PARAMETER_ID,
DEFAULT_VALUE: "new_parameter",
},
"parameterScale": {"strategy": USE_DEFAULT, DEFAULT_VALUE: "log10"},
"lowerBound": {"strategy": MIN_COLUMN},
"upperBound": {"strategy": MAX_COLUMN},
"estimate": {"strategy": USE_DEFAULT, DEFAULT_VALUE: 1},
"nominalValue": {"strategy": SBML_LOOK},
PETAB_C.PARAMETER_SCALE: {
"strategy": USE_DEFAULT,
DEFAULT_VALUE: PETAB_C.LOG10,
},
PETAB_C.LOWER_BOUND: {"strategy": MIN_COLUMN},
PETAB_C.UPPER_BOUND: {"strategy": MAX_COLUMN},
PETAB_C.ESTIMATE: {"strategy": USE_DEFAULT, DEFAULT_VALUE: 1},
PETAB_C.NOMINAL_VALUE: {"strategy": SBML_LOOK},
}
DEFAULT_COND_CONFIG = {
"conditionId": {"strategy": USE_DEFAULT, DEFAULT_VALUE: "new_condition"},
"conditionName": {"strategy": COPY_FROM, SOURCE_COLUMN: "conditionId"},
PETAB_C.CONDITION_ID: {
"strategy": USE_DEFAULT,
DEFAULT_VALUE: "new_condition",
},
PETAB_C.CONDITION_NAME: {
"strategy": COPY_FROM,
SOURCE_COLUMN: PETAB_C.CONDITION_ID,
},
}
DEFAULT_MEAS_CONFIG = {}
DEFAULT_CONFIGS = {
Expand Down
9 changes: 5 additions & 4 deletions src/petab_gui/controllers/default_handler.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,6 +5,7 @@

import numpy as np
import pandas as pd
import petab.v1 as petab

from ..C import (
COPY_FROM,
Expand Down Expand Up @@ -94,9 +95,9 @@ def _min_column(self, column_name, par_scale=None):
if column_name not in self.model:
return ""
column_data = self.model[column_name].replace("", np.nan).dropna()
if column_name in ["upperBound", "lowerBound"]:
if column_name in [petab.C.UPPER_BOUND, petab.C.LOWER_BOUND]:
column_data = column_data.loc[
self.model["parameterScale"] == par_scale
self.model[petab.C.PARAMETER_SCALE] == par_scale
]
if not column_data.empty:
return column_data.min()
Expand All @@ -106,9 +107,9 @@ def _max_column(self, column_name, par_scale=None):
if column_name not in self.model:
return ""
column_data = self.model[column_name].replace("", np.nan).dropna()
if column_name in ["upperBound", "lowerBound"]:
if column_name in [petab.C.UPPER_BOUND, petab.C.LOWER_BOUND]:
column_data = column_data.loc[
self.model["parameterScale"] == par_scale
self.model[petab.C.PARAMETER_SCALE] == par_scale
]
if not column_data.empty:
return column_data.max()
Expand Down
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